Descriptive visualizations

Analysis preparation

  1. load the PISA 2015 dataset.

    PISA is a global assessment conducted every three years, targeting 15-year-olds. It evaluates how well students perform in maths, reading and science. This is an open-sourced dataset, avaliable at:https://www.oecd.org/en/data/datasets/pisa-2015-database.html.

# load the pisa dataset
library(data.table)
## Warning: package 'data.table' was built under R version 4.4.1
pisa <- fread("/Users/yuxinkarlie/Downloads/pisa2015.csv", na.strings = "")

  1. Analysis preparation

    Including data cleaning and data manipulation.

# Rename some variables that's needed later
# Convert a dichtomous item (yes/no) to numeric scoring
bin.to.num <- function(x){
  if (is.na(x)) NA
  else if (x == "Yes") 1L
  else if (x == "No") 0L
}
pisa[, `:=` 
     (female = ifelse(ST004D01T == "Female", 1, 0),
       sex = ST004D01T,
       
       # At my house we have ...
       desk = sapply(ST011Q01TA, bin.to.num),
       own.room = sapply(ST011Q02TA, bin.to.num),
       quiet.study = sapply(ST011Q03TA, bin.to.num),
       computer = sapply(ST011Q04TA, bin.to.num),
       software = sapply(ST011Q05TA, bin.to.num),
       internet = sapply(ST011Q06TA, bin.to.num),
       lit = sapply(ST011Q07TA, bin.to.num),
       poetry = sapply(ST011Q08TA, bin.to.num),
       art = sapply(ST011Q09TA, bin.to.num),
       book.sch = sapply(ST011Q10TA, bin.to.num),
       tech.book = sapply(ST011Q11TA, bin.to.num),
       dict = sapply(ST011Q12TA, bin.to.num),
       art.book = sapply(ST011Q16NA, bin.to.num))]
pisa[, `:=`
     (math = rowMeans(pisa[, c(paste0("PV", 1:10, "MATH"))], na.rm = TRUE),
       reading = rowMeans(pisa[, c(paste0("PV", 1:10, "READ"))], na.rm = TRUE),
       science = rowMeans(pisa[, c(paste0("PV", 1:10, "SCIE"))], na.rm = TRUE))]

# filter the columns we want
country <- c("United States", "Canada", "Mexico", "B-S-J-G (China)", "Japan",
             "Korea", "Germany", "Italy", "France", "Brazil", "Colombia", "Uruguay",
             "Australia", "New Zealand", "Jordan", "Israel", "Lebanon")

dat <- pisa[CNT %in% country,
            .(CNT, OECD, CNTSTUID, W_FSTUWT, sex, female,
              ST001D01T, computer, software, internet,
              ST011Q05TA, ST071Q02NA, ST071Q01NA, ST123Q02NA,
              ST082Q01NA, ST119Q01NA, ST119Q05NA, ANXTEST,
              COOPERATE, BELONG,  EMOSUPS, HOMESCH, ENTUSE,
              ICTHOME, ICTSCH, WEALTH, PARED, TMINS, ESCS,
              TEACHSUP, TDTEACH, IBTEACH, SCIEEFF,
              math, reading, science)
            ]
#Add additional variables
# Let's create additional variables that we will use for visualizations
dat <- dat[, `:=` (
  # New grade variable
  grade = (as.numeric(sapply(ST001D01T, function(x) {
  if(x=="Grade 7") "7"
  else if (x=="Grade 8") "8"
  else if (x=="Grade 9") "9"
  else if (x=="Grade 10") "10"
  else if (x=="Grade 11") "11"
  else if (x=="Grade 12") "12"
  else if (x=="Grade 13") NA_character_
  else if (x=="Ungraded") NA_character_}))),
  # Total learning time as hours
  learning = round(TMINS/60, 0),
  # Regions for selected countries
  Region = (sapply(CNT, function(x) {
  if(x %in% c("Canada", "United States", "Mexico")) "N. America"
    else if (x %in% c("Colombia", "Brazil", "Uruguay")) "S. America"
    else if (x %in% c("Japan", "B-S-J-G (China)", "Korea")) "Asia"
    else if (x %in% c("Germany", "Italy", "France")) "Europe"
    else if (x %in% c("Australia", "New Zealand")) "Australia"
    else if (x %in% c("Israel", "Jordan", "Lebanon")) "Middle-East"
    }))
  )]
# Check the cleaned dataset
head(dat)

Plot the distribution(line plot, violin plot and boxplot) in ggplot.

Dashed line plot

# Dashed line plot
library('ggplot2')
means <- dat[,
             .(math = mean(math)),
             by = CNT]

ggplot(data = dat,
       mapping = aes(x = reorder(CNT, math), y = math)) +
  geom_point(aes(color = Region)) +
  labs(x=NULL, y="Math Scores") +
  coord_flip() +
  stat_summary(fun.y = mean, colour = "blue", geom = "point", 
               shape = 18, size = 2) +
  geom_hline(yintercept = 493, linetype="dashed", color = "red", size = 1) +
  theme_bw()
## Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
## ℹ Please use the `fun` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Violin plot

# Violin pLot
means <- dat[,
             .(math = mean(math)),
             by = CNT]
ggplot(data = dat,
       mapping = aes(x = reorder(CNT, math), y = math)) +
  geom_violin(aes(color = Region)) +
  labs(x=NULL, y="Math Scores") +
  coord_flip() +
  stat_summary(fun.y = mean, colour = "blue", geom = "point", 
               shape = 18, size = 2) +
  geom_hline(yintercept = 493, linetype="dashed", color = "red", size = 1) +
  theme_bw()

Box plot

# Boxlot
ggplot(data = dat,
       mapping = aes(x = reorder(CNT, math), y = math, fill = Region)) +
  geom_boxplot() +
  labs(x=NULL, y="Math Scores") +
  coord_flip() +
  geom_hline(yintercept = 493, linetype="dashed", color = "red", size = 1) +
  stat_summary(fun.y = mean, colour = "blue", geom = "point", 
               shape = 18, size = 2.5) +
  theme_bw()

  • The graphs showed math scores of different country, with colors representing different regions.

  • The red dashed line shows the average math score of countries in

  • The blue square dots indicate the mean.

  • The violin or boxplot could be better compared to line plot, because is shows the distribution information of the data, like visualizing the shape, mean, quantlies and outliers.

Interactive math-by-region density plot

library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
density_plot <- ggplot(data = dat,
       mapping = aes(x = math, fill = Region)) +
  geom_density(alpha = 0.5) +
  labs(x = "Science Scores", y = "Count",
       title = "Math Scores by Gender and Region") +
  facet_grid(. ~ sex) +
  theme_bw()
ggplotly(density_plot)

Hover your mouse on the graph to show detailed information of each region !

Relationship of math score and other factors

Gender Differences and Correlations Among Reading, Science, and Math Scores

library('GGally')
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2
dat_small <- dat[,.SD[sample(.N, min(500,.N))], by = Region]

ggpairs(data = dat_small,
        mapping = aes(color = Region),
        columns = c("reading", "science", "math", "sex"),
        upper = list(continuous = wrap("cor", size = 2.5))
        )
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Interpretation:

  • Generally there is no obvious gender differences across different subjects, but one noticeable difference in science, which is that the male are have more students falling behind.

  • Reading, science, or math are highly correlated to each other.

Exploring the Relationship Between Math Scores and ICT Usage in North America

Explanation of the variables used:

  • HOMESCH: students’ use of ICT outside of school for school work.

  • ENTUSE: students’ use of ICT outside of school for leisure or entertainment purposes.

  • ICTHOME: the availability of ICT resources at students’ homes.(This index is derived from students’ responses to questions about the presence of various ICT devices in their homes, such as desktop computers, laptops, tablets, and internet connections. Each item is assigned a score, and the combined responses are scaled to create the ICTHOME index, ranging from 1-10).

  • ICTSCH: the availability and use of ICT resources at school.

dat_Namerica <- dat[ Region == "N. America"]

ggpairs(data = dat_Namerica,
        mapping = aes(color = sex),
        columns = c("math", "HOMESCH", "ENTUSE", "ICTHOME", "ICTSCH"),
        upper = list(continuous = wrap("cor", size = 2.5))
        )
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 26132 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 26100 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 26646 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 26628 rows containing missing values
## Warning: Removed 26132 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 26132 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 26137 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 26829 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 26805 rows containing missing values
## Warning: Removed 26100 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 26137 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 26100 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 26804 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 26774 rows containing missing values
## Warning: Removed 26646 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 26829 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 26804 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 26646 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 27210 rows containing missing values
## Warning: Removed 26628 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 26805 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 26774 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 27210 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 26628 rows containing non-finite outside the scale range
## (`stat_density()`).

  • The correlations among variables are all significant.

  • The math score has modest relationship with use of Information and Communication Technology (ICT).

  • However, we found a overall correlation is the use of ICT outside of school for leisure(ENTUSE) and for school work(HOMESCH), which is around 0.648. It demonstrate that learning and playing are happening at the same time when students use ICT.

  • Generally male have more access to ICT than female, but their reading scores are lower than female.

Socio-economic status (ESCS) versus math scores (math).

Create a scatter plot of socio-economic status (ESCS) and math scores (math) across regions (region) and gender (sex),with linear regression lines to represent the linear relationship of socio-economic status and math scores.

ggplot(data = dat_small,
       mapping = aes(x = ESCS, y = math)) +
  geom_point() +
  geom_smooth(method = "loess") +
  labs(x = "socio-economic status", y = "math scores") +
  theme_bw() +
  theme(legend.title = element_blank()) +
  facet_grid(sex ~ Region)
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 75 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 75 rows containing missing values or values outside the scale range
## (`geom_point()`).

Alluvial plot of categorical variable

The relationship of survey item (ST119Q01NA) of whether students want top grades in most or all courses by region (Region) and gender (sex).

# Prepare the dataset
dat_alluvial2 <- dat[,
                     .(Freq = .N),
                     by = c("Region", "sex", "ST119Q01NA")
                     ][,
                       ST119Q01NA := as.factor(ifelse(ST119Q01NA == "", "Missing", ST119Q01NA))]
levels(dat_alluvial2$ST119Q01NA) <- c("Strongly disagree", "Disagree", "Agree",
                                      "Strongly agree", "Missing")

dat_alluvial2
# Plot the data
library(ggalluvial)
ggplot(data = dat_alluvial2,
       aes(axis1 = Region, axis2 = ST119Q01NA, y = Freq)) +
  scale_x_discrete(limits = c("Region", "Want top grades"),
                   expand = c(.1, .05)) +
  geom_alluvium(aes(fill = sex)) +
  geom_stratum() +
  geom_text(aes(label = after_stat(stratum)), stat = "stratum") +
  labs(x = "Demographics", y = "Frequency", fill = "Gender") +
  theme_bw()